What Makes an NES Game Valuable?

A Data Analysis of 770 Nintendo Entertainment System Games

Introduction

As a proud member of Generation X, I've always been fascinated by the culture of the 1980s and 1990s. Retro video games, movies, television shows, toys, comic books, fantasy novels, and science fiction stories all played a role in shaping my interests. While many people move on from the hobbies of their youth, I've found myself appreciating them even more as I've gotten older.

Recently, I had the opportunity to purchase a Nintendo Entertainment System (NES) and bring it back to life. Like many aging consoles, it needed some attention. After thoroughly cleaning the system and repairing the infamous 72-pin connector, it was once again ready for action. Since then, I've enjoyed buying, selling, collecting, and playing NES games while learning more about the growing retro gaming market.

At the same time, I've been expanding my data analysis skills and exploring how generative AI can be used to support analytical projects. One question that interested me was simple:

What actually makes an NES game valuable?

More importantly, could generative AI analyze a real dataset and produce meaningful insights without inventing information?

This project became another experiment in understanding how traditional data analysis and generative AI can work together.

Project Objective

The goal of this project was not to determine the value of a specific game. Instead, it was to understand the factors that influence NES game values and evaluate whether generative AI could provide valid insights when given a structured dataset.

The distinction is important.

Generative AI often receives criticism for "making things up" when it lacks access to reliable information. In this project, the AI was not asked to create data. Instead, it was provided with a real database and asked to analyze it.

The question became:

Can generative AI act as a useful data analysis assistant when working with valid source data?

Step 1: Finding a Dataset

The first challenge in any data analysis project is obtaining reliable data.

For this project, I used a publicly available NES rarity and price guide created by members of the retro gaming community.

Sources:

• Video Game Sage NES Rarity Guide

• Published Spreadsheet Database

Source References:

• https://www.videogamesage.com/forums/topic/13798-thephleos-2023-nes-rarity-guide-general-supply-demand-guide/

• https://docs.google.com/spreadsheets/d/e/2PACX-1vR5_eZS_JT5mfAItxURMViFdKtArFp5RnQ1eaQy_pOeZ1AYHUqYuf-8hibPcbKQJHtzXSmPFdRAS6iV/pubhtml

The dataset contained information on hundreds of NES games, including:

• Rarity score

• Licensed or unlicensed status

• Publisher

• Loose cartridge value

• Complete-In-Box (CIB) value

• Availability ranking

Step 2: Preparing the Data

Before any analysis could occur, the spreadsheet required some basic preparation.

The following cleanup activities were performed:

• Verified and cleaned column headers

• Froze the top row for easier navigation

• Reviewed data consistency

• Sorted records by average loose cartridge value

• Identify unusual outliers

• Conduct a quick data check sample by referencing eBay pricing

This process took only a few minutes but reinforced an important lesson in data analysis:

Even simple projects require data preparation before analysis can begin.

Step 3: Exploring the Dataset

Before involving AI, I spent time reviewing the data manually.

A quick review revealed some interesting findings about my own collection.

Most Valuable Loose Game

Mega Man 5

• Value Rank: 58

• Loose Value: Approximately $150

Most Valuable Complete-In-Box Game

Adventures of Lolo 3

• Value Rank: 124

• CIB Value: Approximately $175

While these observations were interesting, they did not explain what drives value across the entire market.

That required a deeper analysis.

Step 4: Using Generative AI for Analysis

Once the dataset was prepared, it was uploaded into ChatGPT and a series of analytical questions were asked.

The questions included:

• Is there a correlation between loose game value and publisher?

• Is there a correlation between loose game value and availability?

• Is there a correlation between rarity score and loose game value?

• What is the average ratio between loose value and CIB value?

• Are unlicensed games valued higher on average than licensed games?

• What overall insight can be drawn from the data?

• Why would someone want to know this information?

The entire process took less than two minutes.

Results: What Actually Makes an NES Game Valuable?

Rarity Matters

One of the strongest findings was the relationship between rarity and value.

Games with higher rarity scores generally commanded higher prices.

This is not surprising. As supply decreases, collectors often become willing to pay more to obtain a game.

However, rarity alone does not tell the entire story.

Availability Matters

Games with fewer copies available in the market tended to have higher values.

In other words:

Scarcity drives price.

When collectors have fewer opportunities to purchase a title, competition among buyers often increases.

Publisher Influence Exists

Publisher also appeared to play a role.

Certain publishers consistently produced games that command higher values today.

However, publisher influence was less significant than rarity and availability and was often impacted by a small number of highly collectible titles.

Unlicensed Games Command Higher Prices

One of the more interesting findings was that unlicensed games were worth more on average than licensed games.

This is likely due to several factors:

• Smaller production runs

• Lower availability

• Collector curiosity

• Historical significance

While not every unlicensed game is valuable, the category as a whole showed stronger average values.

Complete-In-Box Games Are Worth Significantly More

The analysis showed that a loose cartridge is typically worth only about one-quarter of its Complete-In-Box value.

This finding highlights the importance of:

• Original packaging

• Manuals

• Inserts

• Overall completeness

For collectors, condition and completeness often matter as much as the game itself.

Key Insight

The strongest conclusion from this project is simple:

Scarcity drives value, but collector demand determines how much that scarcity matters.

Many games are rare.

Far fewer are both rare and highly desirable.

The most valuable NES games tend to combine:

• Limited availability

• High rarity

• Strong collector demand

• Historical significance

• Nostalgia

• Franchise popularity

The market is influenced by data, but it is also influenced by human behavior.

The Real Lesson: Evaluating Generative AI

While the findings about NES game values were interesting, they were not the most important outcome of the project.

The more significant conclusion was that the analysis itself was valid.

The AI did not create the database.

The AI did not invent the values.

The AI did not estimate rarity scores.

Instead, it performed calculations, identified relationships, and summarized findings based on a structured dataset provided to it.

This distinction is important.

When generative AI is asked to answer questions without data, the results may be questionable.

When generative AI is given a properly structured dataset, it can become an effective analytical tool capable of rapidly identifying trends and generating insights.

In this project, the AI acted less like a search engine and more like a junior analyst reviewing an existing database.

Final Thoughts

This project reinforced a lesson that applies well beyond retro gaming.

Generative AI is not a replacement for data.

It is a tool that becomes significantly more powerful when paired with quality data.

The NES market provided an interesting test case, but the same approach could be applied to:

• Movies

• Comic books

• Trading cards

• Music

• Books

• Business datasets

• Marketing campaigns

The key takeaway is that meaningful analysis still begins with reliable information.

Once that foundation exists, generative AI can dramatically accelerate the process of turning data into insight.

And in this case, it answered a question I've wondered about ever since repairing that old NES:

What actually makes an NES game valuable?

As it turns out, the answer is not just rarity. It is the combination of scarcity, demand, nostalgia, and collector behavior that ultimately determines what people are willing to pay.